Commodity Futures

Do the relationships among returns for stocks and the most heavily traded commodities (gold and crude oil) consistently offer risk diversification? In their July 2013 paper entitled “Gold, Oil, and Stocks”, Jozef Baruník, Evzen Kocenda and Lukas Vacha analyze the return relationships among stocks ( the S&P 500 Index), gold and oil (light crude) over the past 26 years. Specifically, they test the degrees to which their prices: (1) co-move; (2) reliably lead one another; and, share any long-term relationships (such as ratios to which they revert). They seek robustness of findings by employing a variety of methods, data sampling frequencies and investment horizons. Using intraday and daily prices of the most active rolling futures contracts for the S&P 500 Index, gold and light crude oil during 1987 through 2012, they find that: Keep Reading

What do records of actual positions suggest about commodity futures trading success? In the June 2013 version of their paper entitled “Determinants of Trader Profits in Commodity Futures Markets”, Michaël Dewally, Louis Ederington and Chitru Fernando examine actual daily closing positions of energy futures traders to determine how profitability relates to differences in risk taking, trading strategy and information/skill. They assign each trader in their sample to one of eleven categories: refiners, independent producers, pipelines and marketers, large energy consumers, commercial banks, energy traders, hedge funds, households, investment banks and dealers, market makers and others. Using end-of-day open interest for 382 traders reporting positions in NYMEX crude oil, heating oil and gasoline futures markets via the CFTC’s Large Trader Reporting System during June 1993 through March 1997, they find that: Keep Reading

Has crude oil turned into paper from an investment perspective? In their May 2013 paper entitled “Oil Prices, Exchange Rates and Asset Prices”, Marcel Fratzscher, Daniel Schneider and Ine Van Robays examine relationships between crude oil price and behaviors of other asset classes. Specifically, they relate spot West Texas Intermediate (WTI) crude oil price to: the U.S. dollar exchange rate versus a basket of developed market currencies; Dow Jones Industrial Average (DJIA) return; U.S. short-term interest rate; the S&P 500 options-implied volatility index (VIX); and, open interest in the NYMEX crude oil futures (as an indication of financialization of the oil market). They also test the response of crude oil price to economic news. Using daily data for these financial series during January 2001 through mid-October 2012, and contemporaneous U.S. economic news and associated expectations, they find that: Keep Reading

Can commodities still be useful for portfolio diversification, despite their recent poor aggregate return, high volatility and elevated return correlations with other asset classes? In the May 2013 version of their paper entitled “Strategic Allocation to Commodity Factor Premiums”, David Blitz and Wilma de Groot examine the performance and diversification power of the commodity market portfolio and of alternative commodity momentum, carry and low-risk (low-volatility) portfolios. They define the commodity market portfolio as the S&P GSCI (production-weighted aggregation of six energy, seven metal and 11 agricultural commodities). The commodity long-only (long-short) momentum portfolio is each month long the equally weighted 30% of commodities with the highest returns over the past 12 months (and short the 30% of commodities with the lowest returns). The commodity long-only (long-short) carry portfolio is each month long the equally weighted 30% of commodities with the highest annualized ratios of nearest to next-nearest futures contract price (and short the 30% of commodities with the lowest ratios). The commodity long-only (long-short) low-risk portfolio is each month long the equally weighted 30% of commodities with the lowest daily volatilities over the past three years (and short the 30% of commodities with the highest volatilities). They also consider a combination that equally weights the commodity momentum, carry and low-risk portfolios. For comparison to U.S. stocks, they use returns of long-only, equally weighted “big-momentum” and “big-value” (comparable to commodity carry) stock portfolios from Kenneth French, and a similarly constructed “big-low-risk” stock portfolio. For comparison with bonds, they use the total return of the JP Morgan U.S. government bond index. For all return series and allocation strategies, they ignore trading frictions. Using daily and monthly futures index levels and contract prices for the 24 commodities in the S&P GSCI as available during January 1979 through June 2012, along with contemporaneous returns for a broad sample of U.S. stocks, they find that: Keep Reading

It is plausible that crude oil as a dominant energy commodity has return characteristics substantially different from those of other commodities and asset classes, and therefore represents a good diversification opportunity. To check, we add the United States Oil Fund (USO) to the following mix of asset class proxies (the same used in “Simple Asset Class ETF Momentum Strategy”):

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

First, per the findings of “Asset Class Diversification Effectiveness Factors”, we measure the average monthly return for USO and the average pairwise correlation of USO monthly returns with the monthly returns of the above assets. Then, we compare cumulative returns and basic monthly return statistics for equally weighted (EW), monthly rebalanced portfolios with and without USO. We ignore rebalancing frictions, which would be about the same for the alternative portfolios. Using adjusted monthly returns for USO and the above nine asset class proxies as available from May 2006 (first return available for USO) through April 2013 (84 monthly returns), we find that: Keep Reading

A subscriber suggested testing the diversification power of iPath DJ-UBS Copper ETN (JJC) as a distinct asset class. To check, we add JJC to the following mix of asset class proxies (the same used in “Simple Asset Class ETF Momentum Strategy”):

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

First, per the findings of “Asset Class Diversification Effectiveness Factors”, we measure the average monthly return for JJC and the average pairwise correlation of JJC monthly returns with the monthly returns of the above assets. Then, we compare cumulative returns and basic monthly return statistics for equally weighted (EW), monthly rebalanced portfolios with and without JJC. We ignore rebalancing frictions, which would be about the same for the alternative portfolios. Using adjusted monthly returns for JJC and the above nine asset class proxies from November 2007 (first return available for JJC) through April 2013 (66 monthly returns), we find that: Keep Reading

Can investors rely on commodities to protect against inflation? In their May 2013 paper entitled “Commodities as Inflation Protection”, Andrew Marks, George Crawford and Jim Kyung-Soo Liew examine the belief that commodities represent an intrinsic store of value that hedges against inflation. They use spot prices for commodities because these prices have long histories. They use the U.S. Consumer Price Index (CPI) as the measure of inflation. However, CPI is an aggregate of the prices of many finished goods and services adjusted to reflect a government model of the actual cost of living, so it may not be possible to create a portfolio that tracks its composition. Commodities (such as crops, crude oil and precious metals), as raw inputs to these goods and services, may rise in price with the CPI, and the proliferation of futures contracts and related exchange-traded funds (ETF) makes investing in commodities accessible. Using monthly spot prices for 45 individual commodities and 14 aggregating indexes during January 1960 through December 2012 (53 years), with focus on five subperiods involving relatively high inflation, they find that: Keep Reading

Do time series carry (intrinsic value) and time series momentum (intrinsic momentum) strategies work across asset classes? What drives their returns, and how do they interact? In the January 2013 very preliminary version of their paper entitled “The Returns to Carry and Momentum Strategies: Business Cycles, Hedge Fund Capital and Limits to Arbitrage”, Jan Danilo Ahmerkamp and James Grant examine intrinsic value strategy and intrinsic momentum strategy returns for 55 worldwide futures contract series spanning equities, bonds, currencies, commodities and metals, including the effects of business cycle/economic conditions and institutional ownership. They study futures (rather than spot/cash) markets to minimize trading frictions and avoid shorting constraints. They calculate futures contract returns relative to the nearest-to-maturity futures contract (not spot/cash market) price. The momentum signal is lagged 12-month cumulative raw return. The carry (value) signal is the lagged 12-month average normalized price difference between second nearest-to-maturity and nearest contracts. They test strategies that are each month long (short) contracts with positive (negative) value or momentum signals. They also test a combination strategy that is long (short) contracts with both value and momentum signals positive (negative). For comparability of assets, they weight contract series within multi-asset portfolios by inverse volatility, estimated as the average absolute value of daily returns over the past three months. Their benchmark is a long-only portfolio of all contracts weighted by inverse volatility. Using daily settlement prices for the nearest and second nearest futures contracts of the 55 series (10 equities, 12 bonds, 17 commodities, nine currencies and seven metals) as available during 1980 through 2012, they find that: Keep Reading

In her 2012 book A Trader’s First Book on Commodities: An Introduction to The World’s Fastest Growing Market (2nd Edition), author Carley Garner hopes to convey “the realization that anything is possible in the commodity markets. Never say ‘never’ — if you do, you will eventually be proven wrong. Additionally, trading the markets is an art, not a science. Unfortunately, there are no black-and-white answers, nor are there fool-proof strategies — but that does not mean that there aren’t opportunities.” Her further hope is that “this book is the first step in your journey toward victory in the challenging, yet potentially rewarding, commodity markets.” Some notable points from the book are: Keep Reading

Wars both consume crude oil and potentially disrupt supplies. Do they reliably drive up oil price? In their November 2012 paper entitled “Crude Oil as a Safe Haven Asset in Times of War”, Tomasz Wisniewski and Ayman Omar examine the behaviors of crude oil price and stock market indexes around severe international crises and wars. They construct a sample of crises from the July 2010 version of the International Crisis Behavior (ICB) database, excluding events scoring below “6” on the severity scale (such as protests, diplomatic sanctions and withholding economic aid). They also extract a war subsample (border clash/crossing by military forces, invasion of air space, sea/air military operations and large-scale military attacks/bombing). They use the Cushing, Oklahoma West Texas Intermediate (WTI) spot price as crude oil price, but also test the Brent spot price as a robustness check. They consider S&P 500 and MSCI World as representative stock market indexes. They define the crisis/war impact interval as 50 trading days before through 50 trading days after outbreak. They define the effect of a crisis/war on price as the “abnormal” return compared to price behavior during the 150 trading days prior to the impact interval. Using daily crude oil spot price and stock index levels around 64 instances of severe international crises and 43 wars during January 1987 through December 2007, they find that: Keep Reading